Biomolecular Dynamics @ Uni Freiburg


Several of our in-house developments to analyze 'big-data' MD simulations can be obtained from our github


The FastPCA package is an implementation of the principal component analysis of large MD data sets, using either Cartesian atom coordinates, interatom distances or backbone dihedral angles as input coordinates. In particular, it features the dihedral angle PCA on a torus (dPCA+)by Sittel et al., 2017, which performs maximal gap shifting to treat periodic data correctly. It is optimized and parallelized with constant memory consumption for large data sets. Technical information and the source code can be found at the project site.


The clustering package is a highly optimized C++11 program suite parallelized with CUDA and OpenMP. It supports for: The latest version and installation instructions can be found on the project site. Additionally, there is a comprehensive step-by-step technical explanation of a full clustering workflow, based on a sample data set. Further information can be found in the documentation.


The ramacolor script (written in python3) as proposed in Sittel et al., 2016 can be downloaded directly from here. Ramacolor plots visualize the secondary structures of given microstates. Hence, states can be assigned as dynamically active/inactive at a glance.

dissipation-corrected targeted molecular dynamics (dcTMD)

Python scripts used for dissipation-corrected targeted molecular dynamics by Wolf et al., 2018 analysis for usage with "*pullf.xvg" files from Gromacs. More information can be found at the github page.

Data-Driven Langevin Package

We have developed together with R. Hegger a systematic computational approach to describe the conformational dynamics of biomolecules in reduced dimensionality using data-driven Langevin equation modeling.
For details see Hegger et al., 2009 and Schaudinnus et al., 2016.
The software can be downloaded from